@InProceedings{AntunesRAWFEBTLCM:2023:ClInCr,
               author = "Antunes, Jo{\~a}o F. G. and Reis, Aliny A. dos and Almeida, 
                         Henrique S. L. and Werner, Jo{\~a}o P. S. and Figueiredo, Gleyce 
                         K. D. A. and Esquerdo, J{\'u}lio C. D. M. and Bueno, Inacio T. 
                         and Toro, Ana P. S. G. D. D. and Lamparelli, Rubens A. C. and 
                         Coutinho, Alexandre C. and Magalh{\~a}es, Paulo S. G.",
          affiliation = "{Embrapa Agricultura Digital} and {Universidade Estadual de 
                         Campinas (UNICAMP)} and {Universidade Estadual de Campinas 
                         (UNICAMP)} and {Universidade Estadual de Campinas (UNICAMP)} and 
                         {Universidade Estadual de Campinas (UNICAMP)} and {Embrapa 
                         Agricultura Digital} and {Universidade Estadual de Campinas 
                         (UNICAMP)} and {Universidade Estadual de Campinas (UNICAMP)} and 
                         {Universidade Estadual de Campinas (UNICAMP)} and {Embrapa 
                         Agricultura Digital} and {Universidade Estadual de Campinas 
                         (UNICAMP)}",
                title = "Classification of integrated crop-livestock systems using 
                         planetscope time series",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155743",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "nano-satellites, NDVI, EVI, image composites, Multi-Layer 
                         Perceptron.",
             abstract = "The new generation of orbital platforms has increased the 
                         opportunities for land cover classification using time series of 
                         satellite images in the last few years. In this study, we assessed 
                         the performance of high spatial and temporal resolution 
                         PlanetScope (PS) time series to map integrated crop-livestock 
                         systems (ICLS) and different land covers in the western region of 
                         S{\~a}o Paulo State, Brazil. To achieve this goal, 10-day and 
                         15-day composite time series of the vegetation indices on both 
                         pixel and object-level were extracted from the PS images. The land 
                         cover classifications were performed using the Multi-Layer 
                         Perceptron (MLP) classifier, which achieved overall accuracies 
                         greater than 98.0%. The 10-day composite PS time series slightly 
                         outperformed the 15-day composite, returning overall accuracies of 
                         99.1% and 98.6%, respectively. Although our method improved the 
                         discrimination of land parcels with ICLS, prediction maps returned 
                         misclassifications due to the hybrid unit of analysis, which will 
                         be improved in future works with the use of new deep learning 
                         algorithms that fully explore the temporal domain of the time 
                         series.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/495D7BB",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/495D7BB",
           targetfile = "155743.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "16 jun. 2024"
}